CN112188825A - Multi-head chip mounter mounting efficiency optimization method and system based on bat algorithm - Google Patents

Multi-head chip mounter mounting efficiency optimization method and system based on bat algorithm Download PDF

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CN112188825A
CN112188825A CN202011084519.4A CN202011084519A CN112188825A CN 112188825 A CN112188825 A CN 112188825A CN 202011084519 A CN202011084519 A CN 202011084519A CN 112188825 A CN112188825 A CN 112188825A
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唐学峰
邵云峰
李博川
董宁
曹桂平
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Hefei Anxin Precision Technology Co Ltd
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Hefei Itek Photoelectrics Technology Co ltd
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Abstract

The invention discloses a multi-head chip mounter mounting efficiency optimization method and system based on a bat algorithm, wherein S1: constructing a main body code and a hidden layer code which are pasted by a chip mounter; s2: initializing a group of sequences for a main body code by adopting a random integer coding mode to obtain an initialized population; s3: constructing a fitness function, and calculating to obtain the fitness value of each bat in the initialized population to obtain the optimal bat position; s4: adopting Hamming distance to adjust speed update, and enabling the speed of the ith bat in t iterations
Figure DDA0002719917170000011
Associated with a current optimal bat position; s5: calculating a current bat position through position update, the current bat position being a locally optimal path; s6: and (5) carrying out variation on the main body codes, and circulating the steps from S2 to S5 to obtain a global optimal path for mounting of the chip mounter so as to optimize the mountingChip mounting efficiency; by the method and the device, the calculation efficiency and the solution precision for solving the mounting process optimization problem are improved.

Description

Multi-head chip mounter mounting efficiency optimization method and system based on bat algorithm
Technical Field
The invention relates to the technical field of chip mounting devices, in particular to a multi-head chip mounter surface mounting efficiency optimization method and system based on a bat algorithm.
Background
The chip mounter is widely applied to the assembly production line of the printed circuit board, the production speed of one SMT production line is determined by the chip mounter, so that the chip mounter is a core technology part of the whole production line, and the chip mounter has very important practical significance and engineering value for optimizing the component mounting process of the chip mounter and shortening the mounting time. In the actual production process, if the mounting time is too long, the soldering paste printed on the PCB by the screen printing is invalid, so that the reflow soldering effect is poor, and the product quality is seriously influenced.
At present, the algorithm application of the problem of the mounting efficiency of the multi-head arch type chip mounter is roughly divided into two categories, one is a heuristic algorithm, and the other is a group intelligent optimization algorithm. Generally speaking, the swarm intelligence optimization algorithm has better solving quality compared with a heuristic algorithm, but the conventional swarm intelligence optimization algorithms such as a genetic algorithm, an ant algorithm and the like are adopted for solving, so that the problems of low calculation efficiency and poor optimal solution precision are caused by the fact that the individual programming length is long, the calculated amount is exponentially increased along with the increase of the number of elements, the caused defects are more prominent, and the problem of solving by only using the swarm intelligence optimization algorithm is limited to a certain extent. On the other hand, the scale of the problem solved by the swarm intelligence optimization algorithm is usually limited within a limited node number range, so that the optimal solution of the problem in practical application is often unavailable. However, for the heuristic algorithm, the scale of the problem is generally not limited, but the algorithm cannot obtain the optimal solution in most cases, and only can be an approximately optimal solution.
On the other hand, the genetic algorithm is used as a method for solving the mounting optimization problem as a commonly used group intelligent optimization algorithm, but in practical application, the genetic algorithm has the following disadvantages: in the genetic algorithm, the crossover operator carries out crossover operation on individuals by simulating the crossing process of natural organisms, continuously generates new individuals, increases the diversity of population and expands the optimizing range, so that the genetic algorithm has stronger searching capability, but the crossover operator plays a vital role in expanding the solving space of the genetic algorithm and achieving the global optimum. It is because the crossover operator is so important to the performance of genetic algorithms that the design and implementation of the crossover operator is closely related to the problem under study.
In the actual process, commonly used integer Crossover operators include PMX (Partial-Mapped cross), OX (Order cross), PBX (Position-Based cross), OBX (Order-Based cross), CX (cycle cross), and the like, and comparison tests show that when the common integer Crossover operators are used, the search capability is not improved significantly, the convergence speed is low, the solution is high in destructiveness, and the expected optimization effect is not achieved enough. This results in that the worker may spend a lot of time on the algorithm improvement, and it is difficult to have more attention in the actual application scenario, and the work focus is shifted to some extent.
Disclosure of Invention
Based on the technical problems existing in the background technology, in order to improve the calculation efficiency and the solving precision for solving the mounting process optimization problem, the bat algorithm is used as the main algorithm for mounting efficiency optimization, and the calculation efficiency and the solving precision for solving the mounting process optimization problem are further improved by combining with the heuristic algorithm.
A multi-head chip mounter mounting efficiency optimization method based on a bat algorithm comprises the following steps:
s1: constructing a main body code and a hidden layer code for mounting of a chip mounter, wherein the main body code comprises a feeding slot distribution sequence and an element mounting sequence, and the hidden layer code comprises a mounting head distribution sequence and a mounting head suction sequence;
s2: initializing a group of sequences for a main body code by adopting a random integer coding mode to obtain an initialized population;
s3: establishing a fitness function for mounting of the chip mounter by taking the shortest total movement distance and the smallest sucking times of the mounting head for completing the movement in the mounting process as targets, and calculating to obtain the fitness value of each bat in the initialized population to obtain the optimal bat position;
s4: adopting Hamming distance to adjust speed update, and enabling the speed of the ith bat in t iterations
Figure BDA0002719917150000021
Associated with a current optimal bat position;
s5: calculating a current bat position through position update, the current bat position being a locally optimal path;
s6: and (5) carrying out variation on the main body codes, and circulating the steps from S2 to S5 to obtain a global optimal path for mounting of the chip mounter so as to optimize the mounting efficiency of the chip mounter.
Further, the hidden layer coding is calculated by using a heuristic algorithm, and the specific steps are as follows:
s11: the serial number of the feeding groove where the single suction element group is arranged is set as S ═ S1,s2,…,skH (k is less than or equal to H, which represents the total number of mounting heads);
s12: sequencing the number sequence S of the feeding grooves according to a rule from small to large to obtain S' ═ sort (S);
s13: splitting S ' into arrays of (max (S ') -min (S ') +1) lengths, and marking whether an element exists in the feeding groove;
s14: initializing a sliding window, wherein the length of the sliding window is the total number H of the mounting heads, and each sub-window can be stored;
s15: moving the sliding window, scanning a plurality of groups of the numbers in sequence, and recording the total number of the feeding grooves with the elements under the current sliding window;
s16: after scanning is finished, selecting the maximum record number in the total number of the feed slots as a first suction, recording the sucked feed slots, setting the position of the sliding window to be in a non-storable state, and updating and setting corresponding array elements to be empty;
s17: repeating the steps S14-S16 until all array elements are 0;
s18: outputting the element type and the absorbing sequence information of the corresponding mounting head, wherein the absorbing sequence information of the mounting head comprises the absorbing times of the mounting head, the absorbing feed slot ID, the distribution of the mounting head, the absorbing sequence of the mounting head, the absorbing starting position and the absorbing ending position of the mounting head.
Further, the fitness function is modeled as follows:
Figure BDA0002719917150000031
wherein w is a weight coefficient, C is a constant, Δ D is an adjacent pasting rotary head distance, D is a total number of the pasting circulation, piThe times of the mounting head absorbing the element group in the i mounting cycles, w weight coefficient, l0,1For the distance the placement head moves from the initial position to the feed chute in which the 1 st component to be sucked is located,
Figure BDA0002719917150000032
in order to absorb the distance of the common movement of all the elements of the group on the feed chute by the mounting head in i times of the picking and mounting cycles,
Figure BDA0002719917150000033
in order to finish the distance of the common movement of the group of components by the mounting head on the PCB in i times of mounting and removing cycles,
Figure BDA0002719917150000034
the distance the mounting head moves from the ith pick-and-place cycle to the (i +1) th pick-and-place cycle.
Further, at step S4: adopting Hamming distance to adjust speed update, and enabling the speed of the ith bat in t iterations
Figure BDA0002719917150000035
In connection with the current optimal bat position, the hamming distance is specifically: each solution obtained based on the Hamming distance is a one-dimensional array with fixed length, and different solutions are characterized in that the number of the node parts at corresponding positions is different under the condition of the same length.
Further, at step S5: calculating a current bat position through position updating, wherein the current bat position is used as a local optimal path, and the method specifically comprises the following steps:
s51: calculating the Hamming distance between the current bat individual and an optimal bat individual, wherein the optimal bat individual is an optimal bat calculated through a fitness function;
s52: calculating the current bat speed according to the Hamming distance;
s53: if the current bat speed is less than half of the main body coding length, updating the position x of the current bat by a 2-opt position updating formulai；
S54: if the current bat speed is more than or equal to half of the main body coding length, updating the position x of the current bat by a 2-swap position updating formulai。
Further, in step S4, the current bat xiIn the t-th iteration, receive the optimal bat xbestVelocity v obtained by suctiont iComprises the following steps:
Figure BDA0002719917150000041
in step S53, the formula corresponding to the 2-opt position:
Figure BDA0002719917150000042
in step S54, the formula corresponding to the 2-swap position:
Figure BDA0002719917150000043
wherein Hamming distance represents Hamming distance, Random represents Random number, i.e. speed
Figure BDA0002719917150000044
Is a random number from 1 to a hamming distance between them,
Figure BDA0002719917150000045
is the present batxiAt the speed of the t +1 th iteration,
Figure BDA0002719917150000046
is bat xiPosition in the t +1 th iteration.
Further, at S6: the method for varying the main body code and circulating steps from S1 to S5 to obtain the global optimal path for mounting the chip mounter so as to optimize the mounting efficiency of the chip mounter comprises the following steps:
s61: presetting mutation operators of a mutation mechanism, wherein the mutation operators comprise turning, inserting and shifting;
s62: carrying out mutation processing on the current bat through a mutation operator to obtain a mutated bat;
s63: if the random number rand is generated such that rand > RiUpdating the corresponding bat position before the variation by adopting the bat position after the variation;
s64: if the adaptability of the bat position before the variation is better than that of the bat position after the variation and rand is less than RiReceiving the bat position before the current variation, and updating RiAnd AiWherein R isiRepresents the pulse transmission rate and AiRepresenting the pulse emission loudness;
s65: updating the globally optimal bat individual position;
s66: selecting a new bat individual generated by global optimal bat variation, and replacing the bat individual with poor fitness;
s67: and (5) taking the next bat as the current bat, and repeating the steps from S62 to S66 until the set maximum iteration times is reached to obtain the global optimal path for mounting the chip mounter.
A multi-head chip mounter mounting efficiency optimization system based on a bat algorithm comprises a code construction module, an initialization module, a fitness model establishment module, a speed updating module, a position updating module and a variation module;
the code construction module is used for constructing a main body code and a hidden layer code for mounting the chip mounter, wherein the main body code comprises a feeding slot distribution sequence and an element mounting sequence, and the hidden layer code comprises a mounting head distribution sequence and a mounting head absorption sequence and enters the initialization module;
the initialization module is used for initializing a group of sequences for the main body codes in a random integer coding mode to obtain an initialized population and entering the fitness model building module;
the fitness model establishing module is used for establishing a fitness function for mounting the chip mounter by taking the shortest total movement distance of the mounting head in the process of completing mounting and the smallest sucking times of the mounting head as a target, calculating to obtain a fitness value of each bat in an initialized population, obtaining an optimal bat position and entering the updating module;
the speed updating module is used for adjusting the speed updating by adopting the Hamming distance and updating the speed of the ith bat in t iterations
Figure BDA0002719917150000051
Associated with a current optimal bat position;
the position updating module is used for calculating the next iteration position of the current bat through position updating, and the iteration position is used as a local optimal path;
the variation module is used for performing variation on the main body code, circularly enters the initialization module, and obtains a global optimal path for mounting of the chip mounter, so that mounting efficiency of the chip mounter is optimized.
The invention provides a multi-head chip mounter mounting efficiency optimization method and system based on a bat algorithm, which have the advantages that: according to the method and the system for optimizing the mounting efficiency of the multi-head chip mounter based on the bat algorithm, provided by the structure, the codes are divided into the mounting main body codes and the hidden layer codes, so that the verbosity of the codes is avoided, and the overall calculation efficiency is improved; a heuristic algorithm with excellent performance is adopted to generate a high-quality initial solution, the optimal solution can be obtained in a specific interval, then the bat algorithm is used for further optimization, the value of the optimal solution in the specific interval can be obtained, and the accuracy of the optimal solution finally obtained is improved; the bat algorithm adopts a 2-opt or 2-swap position updating formula, realizes the optimization of the discrete bat algorithm and the local search operator in the field together, and ensures that the optimization capability of the algorithm is higher and the convergence is faster. The bat algorithm is selected as the main algorithm for optimizing the mounting efficiency, the bat algorithm has few parameter settings and simple and convenient implementation method, and the mounting optimization problem can be better focused on rather than the optimization aspect of the implementation algorithm.
Drawings
FIG. 1 is a schematic flow chart of the steps of the present invention;
FIG. 2 is a schematic illustration of the minimum number of component draws calculated;
FIG. 3 is a schematic diagram of integer coding to solve a surface mount optimization problem;
FIG. 4 is a schematic view showing calculation of a moving distance in consideration of a pitch of mounting heads;
FIG. 5 is a schematic diagram of an optimization based on 2-opt and 2-swap location update formulas;
FIG. 6 is a schematic diagram of an inverted variant;
FIG. 7 is a schematic illustration of an insertion variation;
FIG. 8 is a schematic diagram of offset variations;
fig. 9 is a framework flow diagram of an embodiment of a multi-arch mount optimized discrete bat algorithm.
Detailed Description
The present invention is described in detail below with reference to specific embodiments, and in the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather should be construed as broadly as the present invention is capable of modification in various respects, all without departing from the spirit and scope of the present invention.
As shown in fig. 1 to 9, a method for optimizing mounting efficiency of a multi-head mounter based on a bat algorithm includes steps S1 to S6:
s1: constructing a main body code and a hidden layer code for mounting of a chip mounter, wherein the main body code comprises a feeding slot distribution sequence and an element mounting sequence, the hidden layer code comprises a mounting head distribution sequence and a mounting head suction sequence,
the hidden layer code is obtained by calculation through a heuristic algorithm; the setting of the hidden layer coding greatly reduces the coding length and improves the calculation efficiency to a certain extent.
In solving the mounting process optimization problem, a coding main body is provided to be composed of two parts of feeding slot distribution and mounting sequence, and as shown in fig. 3, the coding main body is an integer coding schematic diagram for solving the mounting optimization problem, wherein the first part of feeding slot distribution scheme codes, the length of the part of the codes is the total number L of the feeding slots, the sequence number represents the feeding slot number, the corresponding element represents the element type and represents the raw material which can be used according to the element type under the feeding slot number, and when the element value is-1, the raw material is not installed under the feeding slot number; the second part is a component mounting sequence code, the length of the part code is the total number n of the components to be mounted, the sequence number represents the component number, and therefore the total code length is (L + n).
S2: initializing a group of sequences for a main body code by adopting a random integer coding mode to obtain an initialized population;
and initializing a group of sequences by adopting a random integer coding mode for the number of the feeding slot and the number of the surface-mounted element to obtain an initialized population.
S3: establishing a fitness function for mounting of the chip mounter by taking the shortest total movement distance and the smallest sucking times of the mounting head for completing the movement in the mounting process as targets, and calculating to obtain the fitness value of each bat in the initialized population to obtain the optimal bat position;
in calculating the cyclic movement distance of the mounting heads, the position coordinates of each movement are corrected in consideration of the pitch of the mounting heads, and as shown in fig. 4, the distance is calculated by using the euclidean distance and then, the influence of the number of simultaneous suctions on the path is taken into consideration. The fitness function model is therefore:
Figure BDA0002719917150000071
wherein w is a weight coefficient, C is a constant, Δ D is an adjacent pasting rotary head distance, D is a total number of the pasting circulation, piThe times of the mounting head absorbing the element group in the i mounting cycles, w weight coefficient, l0,1For the distance the placement head moves from the initial position to the feed chute in which the 1 st component to be sucked is located,
Figure BDA0002719917150000074
in order to absorb the distance of the common movement of all the elements of the group on the feed chute by the mounting head in i times of the picking and mounting cycles,
Figure BDA0002719917150000075
in order to finish the distance of the common movement of the group of components by the mounting head on the PCB in i times of mounting and removing cycles,
Figure BDA0002719917150000076
the distance the mounting head moves from the ith pick-and-place cycle to the (i +1) th pick-and-place cycle.
S4: adopting Hamming distance to adjust speed update, and enabling the speed of the ith bat in t iterations
Figure BDA0002719917150000077
Linked to the location of the current optimal bat;
defined in Hamming distance-based bat algorithm, bat xiIn the t-th iteration, receive the optimal bat xbestThe speeds obtained by suction are:
Figure BDA0002719917150000072
wherein Hamming distance represents Hamming distance, Random represents Random number, i.e. speed
Figure BDA0002719917150000073
Is a random number from 1 to the hamming distance between the two.
S5: calculating a current bat position through position update, the current bat position being a locally optimal path; the location update specifically includes the following steps S51 to S54:
s51: calculating the Hamming distance between the current bat individual and an optimal bat individual, wherein the optimal bat individual is an optimal bat calculated through a fitness function;
s52: calculating the current bat speed according to the Hamming distance;
S53: if the current bat speed is less than half of the main body coding length, updating the position x of the current bat by a 2-opt position updating formulai(ii) a The 2-opt location update formula is as follows:
Figure BDA0002719917150000081
s54: if the current bat speed is more than or equal to half of the main body coding length, updating the position x of the current bat by a 2-swap position updating formulai. The 2-swap location update formula is as follows:
Figure BDA0002719917150000082
wherein for equations (3) and (4),
Figure BDA0002719917150000083
is the current bat xiAt the speed of the t +1 th iteration,
Figure BDA0002719917150000084
is bat xiPosition in the t +1 th iteration; the position of the bat i at this iteration t +1 is therefore dependent on its speed
Figure BDA0002719917150000085
And position at t iterations
Figure BDA0002719917150000086
The 2-swap position updating formula is not easy to damage the element group, and the effect is better.
S6: and (5) carrying out variation on the main body codes, and circulating the steps from S2 to S5 to obtain a global optimal path for mounting of the chip mounter so as to optimize the mounting efficiency of the chip mounter.
The feasible solution coding obtained through S5 in step S1 has the characteristic of a good "coding block", but the dynamic path segment may be only a locally optimal path, and a variation mechanism is introduced to solve the local optimal path that may be caused. The variation mechanism only varies the feeding slot distribution coding segment, or varies the component mounting sequence coding segment, or simultaneously sends variation conditions to the feeding slot distribution coding segment and the component mounting sequence coding segment, and the variation mechanism is respectively adopted for each gene segment, so as to achieve the purpose of optimizing the feeding slot distribution and mounting sequence.
Therefore, step S6 specifically includes the following steps: s61: presetting mutation operators of a mutation mechanism, wherein the mutation operators comprise turning, inserting and shifting;
s62: carrying out mutation processing on the current bat through a mutation operator to obtain a mutated bat;
s63: if the random number rand is generated such that rand > RiUpdating the corresponding bat position before the variation by adopting the bat position after the variation;
when rand ═ RiAnd meanwhile, the bat position after the variation is adopted to update the corresponding bat position before the variation.
S64: if the adaptability of the bat position before the variation is better than that of the bat position after the variation and rand is less than RiReceiving the bat position before the current variation, and updating RiAnd AiWherein R isiRepresents the pulse transmission rate and AiRepresenting the pulse emission loudness;
s65: updating the globally optimal bat individual position;
s66: selecting a new bat individual generated by global optimal bat variation, and replacing the bat individual with poor fitness;
s67: and (5) taking the next bat as the current bat, and repeating the steps from S62 to S66 until the set maximum iteration times is reached to obtain the global optimal path for mounting the chip mounter.
The bat referred by the application is corresponding to different solutions obtained by solving the mounting path, the bat individual with good adaptability continuously replaces the bat individual with poor adaptability, and the optimal bat individual is obtained through cyclic iteration, and the bat individual corresponds to the required optimal solution, so that the optimal mounting path is obtained.
Further, the basic principle of the heuristic algorithm for calculating the hidden layer coding (mounting head assignment and mounting head suction order) information in S1 is as follows: when the components in the designated feeding slot number are sucked by the multiple mounting heads, an algorithm of the minimum sucking times of the components is designed, and when the mounting heads suck a plurality of components simultaneously, the sucking action time and the sucking moving time can be greatly reduced, and the mounting efficiency can be greatly improved. The specific steps of the heuristic algorithm are as follows S11-S17:
s11: the serial number of the feeding groove where the single suction element group is arranged is set as S ═ S1,s2,…,skH (k is less than or equal to H, which represents the total number of mounting heads);
s12: sequencing the number sequence S of the feeding grooves according to a rule from small to large to obtain S' ═ sort (S);
s13: splitting S ' into arrays of (max (S ') -min (S ') +1) lengths, and marking whether an element exists in the feeding groove; whether an element exists is represented by a different numerical element, such as 1 for containing an element, and 0 for null.
S14: initializing a sliding window, wherein the length of the sliding window is the total number H of the mounting heads, and each sub-window can be stored;
s15: moving the sliding window, scanning a plurality of groups of the numbers in sequence, and recording the total number of the feeding grooves with the elements under the current sliding window;
the total number of feed slots with elements under the current sliding window is recorded, i.e. corresponds to the total number of elements under the current window being recorded as 1.
S16: after scanning is finished, selecting the maximum record number in the total number of the feed slots as a first suction, recording the sucked feed slots, setting the position of the sliding window to be in a non-storable state, and updating and setting corresponding array elements to be empty; null corresponds to the number 0;
s17: repeating the steps S14-S16 until all array elements are 0;
s18: outputting the element type and the absorbing sequence information of the corresponding mounting head, wherein the absorbing sequence information of the mounting head comprises the absorbing times of the mounting head, the absorbing feed slot ID, the distribution of the mounting head, the absorbing sequence of the mounting head, the absorbing starting position and the absorbing ending position of the mounting head.
To describe the flow of steps S11 to S18 in more detail, as shown in fig. 2, assuming that the total number of mounting heads is 5 and the number sequence of the single-suction feeding grooves is S ═ 5,7,3,8,9, S is first sorted from small to large to obtain S' ═ 3,5,7,8, 9; converting S' into an array with the length of 7, wherein the element of each position of the array is Sary ═ {1,0,1,0,1,1,1}, and 1 represents that the position contains an element; the length of an initialized sliding window is 5, the sliding window inquires the position with the most elements 1 in sequence, 3 elements are found under the sliding window at the starting position, 3 elements can be simultaneously absorbed, the sliding window reaches the array position 5, 4 elements are found under the current window, and the inquiry is finished. It can be seen that the maximum number of elements is sucked when the window is at array position 5.
Therefore, the suction information of the mounting head includes the following parameters: firstly, the absorbing frequency of the mounting head is 2, and secondly, the corresponding relation of the number of the feeding groove absorbed by the mounting head is as follows: { H1 → 5, H2 → 3, H3 → 7, H4 → 8, H5 → 9}, and the corresponding relation of the suction sequence of the placement head is: { H1 → 1, H2 → 2, H3 → 1, H4 → 1, H5 → 1}, and (iv) the starting position and the ending position of the suction by the mounting head in accordance with the mounting head suction order. The mounting header information is required to calculate the fitness value in connection with calculating the fitness function.
Further, the specific step of calculating the formula fitness value in S3 is: first, information of component suction is calculated, that is, according to the feeder assignment and component mounting sequence in the body code, according to the pick-and-place cycle group, the suction times, the suction feed slot ID, the placement head assignment, the suction order, and the start position and the end position of suction are calculated by using the minimum suction time algorithm of the above steps S11 to S18, and by combining the component mounting sequence in the body code, the placement head moving distance in a group of pick-and-place cycles is calculated, and by analogy, the fitness value is obtained by accumulation.
Further, redefining v using Hamming distance in S4iThe method of (1) is that in the mounting sequence problem, each solution is a one-dimensional array with fixed length, and different solutions are characterized by different numbers of node parts at corresponding positions under the condition of the same length. Taking the problem of mounting order of 8 components as an example, two feasible solutions x are provided1＝[1 3 4 6 2 5 8 7]And x2＝[1 7 4 6 3 5 2 8]Then the Hamming distance between the two solutionsIs 4. If the two solutions are regarded as two bats, the Hamming distance of 4 units is needed for one bat flying to the other bat, namely the Hamming distance 4 is recorded as Hamming distance (x)1,x2)＝4。
Further, the 2-opt or 2-swap location update formulas in steps S53 and S54 both belong to one of local searches, and each bat checks x of its neighboring bats in each iterated populationiAnd selecting the best one as the current moving target, the bat i selects the best position to move after executing the 2-opt or 2-swap position updating formula.
As shown in FIG. 5, the implementation of the 2-opt and 2-swap location update formulas is schematically illustrated. Specifically, the 2-opt position updating formula is to improve the current path by exchanging 2 edges each time for a given initial component mounting sequence, assume that (i, i +1) and (j, j +1) are two edges of the current path, obtain two new edges (i, j +1) and (j, i +1) after 2-opt operation, and invert the original path between i +1 and j to obtain a new solution.
The 2-swap position updating formula is to exchange two vertexes in a line to form a new line. The realized process is as follows: for a given initial component mounting order, a new solution is obtained by swapping 2 vertices at a time, without changing the original vertex order of the initial component mounting order.
Further, the mutation operator used in S6 is (r) inverted: randomly selecting 2 vertexes in the code, and turning over the sequence between the vertexes, wherein the process is shown in FIG. 6; inserting: randomly selecting a vertex and an insertion position, and moving the vertex to the insertion position to form a new code, wherein the process is shown in FIG. 7; ③ offsetting: randomly selecting a vertex and a random length, and moving the random length segment to a new position, the process is shown in fig. 8.
According to the steps S1 to S6, the mounting process optimization problem is a typical discrete optimization problem, and when the mounting process optimization problem of the chip mounter is calculated, the situation that the calculation efficiency is low and the accuracy of the optimal solution is poor exists only by adopting the bat algorithm for solving, and the calculation amount increases exponentially as the number of mounted components increases, so that the caused defects are more prominent. Therefore, the method comprehensively uses the technologies of the discrete bat algorithm, the neighborhood algorithm and the like, and the bat position and speed in the classic bat algorithm are redefined through the variable method mode of updating and the relevant operation in the algorithm, so that the bat position and speed are discretized; the optimization speed of the mounting process of the multi-head arch type chip mounter is greatly improved, and the problems of low calculation efficiency, poor precision and the like of the existing mounting optimization method are solved.
In summary, as shown in fig. 9, an embodiment of a mounting optimization method for a mounter is as follows:
s100: constructing a main body code and a hidden layer code which are pasted by a chip mounter, and performing population initialization processing on the main body code based on a heuristic algorithm to obtain an initialized population;
s200: calculating the fitness of each bat, and obtaining and temporarily storing the optimal bat position through the fitness comparison;
s300: calculating the Hamming distance between the current bat and the optimal bat, and calculating the current bat speed according to the Hamming distance;
s400: judging whether the acquired speed is less than half of the main body coding length, if so, entering a step S500, and if not, entering a step S600;
updating the position x of the current bat by adopting a 2-opt or 2-swap position updating formula according to the speed judgmenti；
S500: updating the position x of the current bat through a 2-opt position updating formulai；
S600: updating the position x of the current bat through a 2-swap position updating formulai；
S700: carrying out mutation operation on the current bat by adopting a mutation mechanism;
s800: if rand>RiThe changed bat is used for updating the original bat position;
s900: if the adaptability of the original bat position is better and rand<AiReceiving the current original bat position, and updating RiAnd Ai；
S110: updating a globally optimal bat location;
s120: selecting the optimal bat to generate a new bat individual through variation, and replacing the bat with poor adaptability;
s130: and repeating the steps S200-S120 until the maximum iteration times is reached to obtain the optimal path for the surface mounting of the surface mounting machine.
A multi-head chip mounter mounting efficiency optimization system based on a bat algorithm is characterized by comprising a code construction module, an initialization module, a fitness model establishment module, a speed updating module, a position updating module and a variation module;
the code construction module is used for constructing a main body code and a hidden layer code for mounting the chip mounter, wherein the main body code comprises a feeding slot distribution sequence and an element mounting sequence, and the hidden layer code comprises a mounting head distribution sequence and a mounting head absorption sequence and enters the initialization module;
the initialization module is used for initializing a group of sequences for the main body codes in a random integer coding mode to obtain an initialized population and entering the fitness model building module;
the fitness model establishing module is used for establishing a fitness function for mounting the chip mounter by taking the shortest total movement distance of the mounting head in the process of completing mounting and the smallest sucking times of the mounting head as a target, calculating to obtain a fitness value of each bat in an initialized population, obtaining an optimal bat position and entering the updating module;
the speed updating module is used for adjusting the speed updating by adopting the Hamming distance and updating the speed of the ith bat in t iterations
Figure BDA0002719917150000121
Associated with a current optimal bat position;
the position updating module is used for calculating the next iteration position of the current bat through position updating, and the iteration position is used as a local optimal path;
the variation module is used for performing variation on the main body code, circularly enters the initialization module, and obtains a global optimal path for mounting of the chip mounter, so that mounting efficiency of the chip mounter is optimized.
Compared with the prior art, the invention has the following technical advantages:
according to the method, the bat algorithm is selected as the main algorithm for optimizing the mounting efficiency, the bat algorithm has few parameter settings and a simple implementation method, the mounting optimization problem can be better focused on, the optimization aspect of the algorithm is not realized, the codes are divided into the mounting main body codes and the hidden layer codes, the verbosity of the codes is avoided, and the overall calculation efficiency is improved; a heuristic algorithm with excellent performance is adopted to generate a high-quality initial solution, the optimal solution can be obtained in a specific interval, then the bat algorithm is used for further optimization, the value of the optimal solution in the specific interval can be obtained, and the accuracy of the optimal solution finally obtained is improved; the bat algorithm speed judgment adopts a 2-opt or 2-swap position updating formula bat position, and realizes the optimization of the discrete bat algorithm and the local search operator in the field together, so that the algorithm has higher optimization capability and faster convergence.
The scheme of the embodiment of the invention comprehensively uses the technologies of the discrete bat algorithm, the neighborhood local search operator and the like, greatly improves the mounting process optimization speed of the multi-head arch type chip mounter through reasonable modeling and design, and solves the problems of low calculation efficiency, poor precision and the like of the existing mounting optimization method.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.

Claims (8)

1. A multi-head chip mounter mounting efficiency optimization method based on a bat algorithm is characterized by comprising the following steps:
s1: constructing a main body code and a hidden layer code for mounting of a chip mounter, wherein the main body code comprises a feeding slot distribution sequence and an element mounting sequence, and the hidden layer code comprises a mounting head distribution sequence and a mounting head suction sequence;
s2: initializing a group of sequences for a main body code by adopting a random integer coding mode to obtain an initialized population;
s3: establishing a fitness function for mounting of the chip mounter by taking the shortest total movement distance and the smallest sucking times of the mounting head for completing the movement in the mounting process as targets, and calculating to obtain the fitness value of each bat in the initialized population to obtain the optimal bat position;
s4: adopting Hamming distance to adjust speed update, and enabling the speed v of the ith bat in t iterationsit is linked to the current optimal bat position;
s5: calculating a current bat position through position update, the current bat position being a locally optimal path;
s6: and (5) carrying out variation on the main body codes, and circulating the steps from S2 to S5 to obtain a global optimal path for mounting of the chip mounter so as to optimize the mounting efficiency of the chip mounter.
2. The method for optimizing the mounting efficiency of the multi-head chip mounter based on the bat algorithm as claimed in claim 1, wherein the hidden layer code is calculated by using a heuristic algorithm, and the specific steps are as follows:
s11: the serial number of the feeding groove where the single suction element group is arranged is set as S ═ S1,s2,…,skWherein k is less than or equal to H, and H represents the total number of the mounting heads;
s12: sequencing the number sequence S of the feeding grooves according to a rule from small to large to obtain S' ═ sort (S);
s13: splitting S ' into arrays of (max (S ') -min (S ') +1) lengths, and marking whether an element exists in the feeding groove;
s14: initializing a sliding window, wherein the length of the sliding window is the total number H of the mounting heads, and each sub-window can be stored;
s15: moving the sliding window, scanning a plurality of groups of the numbers in sequence, and recording the total number of the feeding grooves with the elements under the current sliding window;
s16: after scanning is finished, selecting the maximum record number in the total number of the feed slots as a first suction, recording the sucked feed slots, setting the position of the sliding window to be in a non-storable state, and updating and setting corresponding array elements to be empty;
s17: repeating the steps S14-S16 until all array elements are 0;
s18: outputting the element type and the absorbing sequence information of the corresponding mounting head, wherein the absorbing sequence information of the mounting head comprises the absorbing times of the mounting head, the absorbing feed slot ID, the distribution of the mounting head, the absorbing sequence of the mounting head, the absorbing starting position and the absorbing ending position of the mounting head.
3. The method of claim 1, wherein the fitness function model is as follows:
Figure FDA0002719917140000021
wherein w is a weight coefficient, C is a constant, Δ D is an adjacent pasting rotary head distance, D is a total number of the pasting circulation, piThe times of the mounting head absorbing the element group in the i mounting cycles, w weight coefficient, l0,1For the distance the placement head moves from the initial position to the feed chute in which the 1 st component to be sucked is located,
Figure FDA0002719917140000022
in order to absorb the distance of the common movement of all the elements of the group on the feed chute by the mounting head in i times of the picking and mounting cycles,
Figure FDA0002719917140000023
in order to finish the distance of the common movement of the group of components by the mounting head on the PCB in i times of mounting and removing cycles,
Figure FDA0002719917140000024
the distance the mounting head moves from the ith pick-and-place cycle to the (i +1) th pick-and-place cycle.
4. The method for optimizing the mounting efficiency of a multi-head chip mounter based on a bat algorithm as claimed in claim 1, wherein in step S4: speed update with hamming distance adjustmentThe speed of the ith bat in t iterations
Figure FDA0002719917140000025
In connection with the current optimal bat position, the hamming distance is specifically: each solution obtained based on the Hamming distance is a one-dimensional array with fixed length, and different solutions are characterized in that the number of the node parts at corresponding positions is different under the condition of the same length.
5. The method for optimizing the mounting efficiency of a multi-head chip mounter based on a bat algorithm as claimed in claim 1, wherein in step S5: calculating a current bat position through position updating, wherein the current bat position is used as a local optimal path, and the method specifically comprises the following steps:
s51: calculating the Hamming distance between the current bat individual and an optimal bat individual, wherein the optimal bat individual is an optimal bat calculated through a fitness function;
s52: calculating the current bat speed according to the Hamming distance;
s53: if the current bat speed is less than half of the main body coding length, updating the position x of the current bat by a 2-opt position updating formulai；
S54: if the current bat speed is more than or equal to half of the main body coding length, updating the position x of the current bat by a 2-swap position updating formulai。
6. The method of optimizing placement efficiency for a multi-head chip mounter based on a bat algorithm as claimed in claim 5, wherein in step S4, the current bat x isiIn the t-th iteration, receive the optimal bat xbestVelocity v obtained by suctiont iComprises the following steps:
Figure FDA0002719917140000031
in step S53, the formula corresponding to the 2-opt position:
Figure FDA0002719917140000032
in step S54, the formula corresponding to the 2-swap position:
Figure FDA0002719917140000033
wherein HammingDistance represents Hamming distance, Random represents Random number, i.e. velocity vt iIs a random number from 1 to a hamming distance between them,
Figure FDA0002719917140000034
is the current bat xiAt the speed of the t +1 th iteration,
Figure FDA0002719917140000035
is bat xiPosition in the t +1 th iteration.
7. The method for optimizing the mounting efficiency of a multi-head chip mounter based on a bat algorithm as claimed in any one of claims 1 to 6, wherein at S6: the method for varying the main body code and circulating steps from S1 to S5 to obtain the global optimal path for mounting the chip mounter so as to optimize the mounting efficiency of the chip mounter comprises the following steps:
s61: presetting mutation operators of a mutation mechanism, wherein the mutation operators comprise turning, inserting and shifting;
s62: carrying out mutation processing on the current bat through a mutation operator to obtain a mutated bat;
s63: if the random number rand is generated such that rand > RiUpdating the corresponding bat position before the variation by adopting the bat position after the variation;
s64: if the adaptability of the bat position before the variation is better than that of the bat position after the variation and rand is less than RiReceiving the bat position before the current variation, and updating RiAnd AiWherein R isiIndicating the pulse firing rateRatio and AiRepresenting the pulse emission loudness;
s65: updating the globally optimal bat individual position;
s66: selecting a new bat individual generated by global optimal bat variation, and replacing the bat individual with poor fitness;
s67: and (5) taking the next bat as the current bat, and repeating the steps from S62 to S66 until the set maximum iteration times is reached to obtain the global optimal path for mounting the chip mounter.
8. A multi-head chip mounter mounting efficiency optimization system based on a bat algorithm is characterized by comprising a code construction module, an initialization module, a fitness model establishment module, a speed updating module, a position updating module and a variation module;
the code construction module is used for constructing a main body code and a hidden layer code for mounting the chip mounter, wherein the main body code comprises a feeding slot distribution sequence and an element mounting sequence, and the hidden layer code comprises a mounting head distribution sequence and a mounting head absorption sequence and enters the initialization module;
the initialization module is used for initializing a group of sequences for the main body codes in a random integer coding mode to obtain an initialized population and entering the fitness model building module;
the fitness model establishing module is used for establishing a fitness function for mounting the chip mounter by taking the shortest total movement distance of the mounting head in the process of completing mounting and the smallest sucking times of the mounting head as a target, calculating to obtain a fitness value of each bat in an initialized population, obtaining an optimal bat position and entering the updating module;
the speed updating module is used for adjusting the speed updating by adopting the Hamming distance and updating the speed of the ith bat in t iterations
Figure FDA0002719917140000041
Associated with a current optimal bat position;
the position updating module is used for calculating the next iteration position of the current bat through position updating, and the iteration position is used as a local optimal path;
the variation module is used for performing variation on the main body code, circularly enters the initialization module, and obtains a global optimal path for mounting of the chip mounter, so that mounting efficiency of the chip mounter is optimized.
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